Tileries

0th

Percentile

Production of Tileries in Egypt

weeklyly observations of 25 firms

number of observations : 483

number of time-series : 22

country : Egypt

JEL codes: D24, C13, C51, C23, J31

Chapter : 01, 03

Keywords
datasets
Usage
data(Tileries)
Format

A dataframe containing:

id

firm id

week

week (3 weeks aggregated)

area

one of "fayoum" and "kalyubiya"

output

output

labor

labor hours

machine

machine hours

References

Horrace and Schmidt (1996) “Confidence Statements for Efficiency Estimates From Stochastic Frontier Models”, Journal of Productity Analysis, 7, 257--282, 10.1007/BF00157044 .

Horrace and Schmidt (2012) “Multiple Comparisons with the Best, with Economic Applications”, Journal of Applied Econometrics, 15(1), 1--26, 10.1002/(SICI)1099-1255(200001/02)15:1<1::AID-JAE551>3.0.CO;2-Y .

Seale J.L. (1990) “Estimating Stochastic Frontier Systems with Unbalanced Panel Data: the Case of Floor Tile Manufactories in Egypt”, Journal of Applied Econometrics, 5, 59--79, 10.1002/jae.3950050105 .

Aliases
  • Tileries
Examples
# NOT RUN {
#### Example 1-2

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
library("plm")
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             subset = area == "fayoum")))

## ------------------------------------------------------------------------
coef(summary(plm(log(output) ~ log(labor) + machine, data = Tileries,
             model = "pooling", subset = area == "fayoum")))


#### Example 1-5

## ------------------------------------------------------------------------
data("Tileries", package = "pder")
til.fm <- log(output) ~ log(labor) + log(machine)
lm.mod <- lm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
library(car)
lht(lm.mod, "log(labor) + log(machine) = 1")

## ------------------------------------------------------------------------
library(car)
lht(lm.mod, "log(labor) + log(machine) = 1", vcov=vcovHC)


#### Example 1-6

## ------------------------------------------------------------------------
plm.mod <- plm(til.fm, data = Tileries, subset = area == "fayoum")

## ------------------------------------------------------------------------
library(car)
lht(plm.mod, "log(labor) + log(machine) = 1", vcov = vcovHC)

#### Example 3-1

## ------------------------------------------------------------------------
library(plm)
data("Tileries", package = "pder")
head(Tileries, 3)
pdim(Tileries)

## ------------------------------------------------------------------------
Tileries <- pdata.frame(Tileries)
plm.within <- plm(log(output) ~ log(labor) + log(machine), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
lm.within <- lm(I(y - Between(y)) ~ I(x1 - Between(x1)) + I(x2 - Between(x2)) - 1)
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) + factor(id), Tileries)
coef(lm.lsdv)[2:3]
coef(lm.within)
coef(plm.within)

## ------------------------------------------------------------------------
tile.r <- plm(log(output) ~ log(labor) + log(machine), Tileries, model = "random")
summary(tile.r)

## ------------------------------------------------------------------------
plm.within <- plm(log(output) ~ log(labor) + log(machine),
                  Tileries, effect = "twoways")
lm.lsdv <- lm(log(output) ~ log(labor) + log(machine) +
                  factor(id) + factor(week), Tileries)
y <- log(Tileries$output)
x1 <- log(Tileries$labor)
x2 <- log(Tileries$machine)
y <- y - Between(y, "individual") - Between(y, "time") + mean(y)
x1 <- x1 - Between(x1, "individual") - Between(x1, "time") + mean(x1)
x2 <- x2 - Between(x2, "individual") - Between(x2, "time") + mean(x2)
lm.within <- lm(y ~ x1 + x2 - 1)
coef(plm.within)
coef(lm.within)
coef(lm.lsdv)[2:3]

## ------------------------------------------------------------------------
wh <- plm(log(output) ~ log(labor) + log(machine), Tileries,
          model = "random", random.method = "walhus",
          effect = "twoways")
am <- update(wh, random.method = "amemiya")
sa <- update(wh, random.method = "swar")
ercomp(sa)

## ------------------------------------------------------------------------
re.models <- list(walhus = wh, amemiya = am, swar = sa)
sapply(re.models, function(x) sqrt(ercomp(x)$sigma2))
sapply(re.models, coef)

# }
Documentation reproduced from package pder, version 1.0-1, License: GPL (>= 2)

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